UC Santa Barbara

Are chimpanzees able to inhibit anti-social behavior towards their friends? And how do they react when a friend acts anti-socially towards them? Past work has demonstrated that chimpanzees are selectively prosocial towards their friends: they groom them more, help them more, and share more food with them. However, the influence of friendship on anti-social behavior remains underexplored. Here, we use food theft as a test case to examine (1) the influence of friendship on anti-social behavior and (2) emotional responses to anti-social behavior. We assigned 26 chimpanzees to pairs of friends (friend condition) and non-friends (non-friend condition) based on 800 h of observations of naturally occurring interactions. The pairs then participated in a stealing task, in which one chimpanzee (the “thief”) could pull a tray with pieces of pineapple, on which the other was feeding (the “victim”). Friends and non-friends were matched on sex and dominance rank. We found that chimpanzees stole less from their friends. When theft did occur, chimpanzees tended to initially express more negative emotions in the friend condition than in the non-friend condition, but then attenuated this response with increasing trial number. Thieves, meanwhile, expressed more negative emotions in the non-friend condition, suggesting that they treated interactions with non-friends as more hostile. These results speak to the complex role of friendship in regulating anti-social behavior and emotional responses to it.

Matrix-bound nanovesicles (MBVs) are a recently discovered subclass of small extracellular vesicles (EVs) that reside within the extracellular matrix of non-mineralized tissues throughout the body. Functionally, MBVs exhibit unique immunomodulatory properties that have been leveraged therapeutically to treat various tissue pathologies, including periprosthetic osteolysis, rheumatoid arthritis, and skeletal muscle injury. However, like other EVs, the therapeutic efficacy of MBV applications is limited by delivery methods, namely bolus injections, that offer poor control of EV persistence and bioavailability at the site of administration. We hypothesized that a superior MBV delivery platform could be developed by entrapping MBVs in a tunable, engineered alginate matrix to control retention and release of MBVs on therapeutically relevant timescales. To this end, we encapsulated dermal fibroblast MBVs in bioinert alginate hydrogels with varying stiffness and stress relaxation rates to determine the impact of matrix mechanical properties on MBV release and retention over a 14-day period. We found that stiffer matrices increased MBV release and decreased MBV retention compared to their softer counterparts. Additionally, fast-relaxing matrices retained fewer MBVs than slow-relaxing matrices regardless of differences in matrix stiffness. We then compared our results to synthetic liposomes and found these results mirror our MBV results, demonstrating that alginate materials may be utilized for more precise control of EV and liposome delivery in the body and may overcome limitations associated with current EV administration methods.

Inferring mechanical interactions from the spatial organization of discrete damage events is a fundamental inverse problem in the mechanics of heterogeneous materials. This work develops a statistical framework for extracting load-transfer interactions from three-dimensional fiber fracture data in unidirectional fiber-reinforced composites. Conventional micromechanical models typically assume global load sharing (GLS), implying statistically independent fiber fracture events, whereas growing experimental evidence suggests that load redistribution can become localized, producing spatially autocorrelated fracture. To distinguish these effects, classical second-order spatial statistics are extended by decomposing pairwise separations into transverse and axial components, reflecting the directional asymmetry of stress redistribution in fiber composites. Analytical solutions are derived for two canonical GLS models that establish a mechanics-based null hypothesis for mechanical independence. Unlike conventional approaches based on complete spatial randomness, this null hypothesis explicitly accounts for both the underlying fiber arrangement and the observed axial break intensity profile, allowing arbitrary fracture distributions to be analyzed without assuming a particular axial form. A normalized fiber interaction statistic is then introduced to remove these contributions, enabling direct assessment of mechanically induced coupling between neighboring fibers and meaningful comparison among datasets with different fiber arrangements and break distributions. Monte Carlo simulations quantify estimator variability and demonstrate that localized interactions can be detected with high statistical power even when only a modest fraction of fiber breaks are mechanically coupled. The resulting framework provides a rigorous methodology for interpreting volumetric fracture data and establishes a quantitative bridge between experimentally observed spatial damage patterns and micromechanical load-sharing models. Although demonstrated for fiber fracture in unidirectional composites, the underlying methodology is broadly applicable to heterogeneous materials in which damage evolves through discrete spatial events.

Recognition of large f-block ions underlies advances in targeted radionuclide therapy and rare-earth element separations. Like the lanthanide-binding protein lanmodulin, the 18-membered macrocycle macropa displays reverse-size selectivity characterized by its preference for binding large metal ions. Its diaza-18-crown-6 scaffold enables efficient complexation of therapeutically relevant radiometals, such as ²²⁵Ac, ²²³Ra, and ²¹³Bi, while accommodating diagnostic partners, including ²⁰³Pb and radiolanthanides. In parallel, systematic differences in stability constants across the lanthanide series enable size-based discrimination in separation chemistry. Macropa and its derivatives have been deployed in different strategies to recover and purify rare-earth elements and minor actinides. This manuscript describes recent studies on modifications of macropa, including cavity expansion, alteration of donor atoms, backbone rigidification, chelator-embedded ¹⁸F incorporation, and acyclic variants to demonstrate that effective selectivity arises from balancing preorganization, donor strength, and conformational adaptability. These studies establish macropa-based scaffolds as synthetic systems that bridge radiopharmaceutical coordination chemistry and rare-earth element separations.

In the context of a knowledge economy and innovation-driven development, innovation districts have emerged as place-based urban development tools increasingly embedded within the urban redevelopment of old districts. While this economically and industrially oriented redevelopment pattern revitalizes urban spaces, it also raises concerns about social integration and community inclusion. This study proposes using cognitive regions as a key lens to assess whether urban redevelopment trends toward social integration or segregation, examining the cognitive coupling that occurs when innovation-driven selective redevelopment is inserted into the context of Chinese urban villages. Using Tangdong Village in Guangzhou as a case study, we apply an enhanced cognitive interview (ECI) method combined with visual and spatial analyses to identify cognitive disconnections between different groups toward original residential and innovation spaces, manifested in differences in cognitive elements, focal points, core ranges, and meaning interpretations. At the same time, the transitional streets and environmental improvements surrounding the innovation spaces are weakening the perceived boundaries between the two. We further outline the potential phased evolution of the cognitive impacts of this pattern and highlight that balancing the needs of different groups throughout this process is key to achieving inclusive and sustainable redevelopment.

This study develops an integrative methodological framework that combines forensic hydrology, artificial neural networks (ANNs), and physically-based hydrological modeling to assess the cascading impacts of projected land use/land cover (LULC) changes on future flood dynamics in the upstream watershed of Golestan Dam, Iran. A large-magnitude flood event analogous to the destructive 2019 flood was simulated under evolving LULC scenarios for three future horizons: 2030, 2040, and 2050. The curve number (CN) and lag time (Tlag) were reconstructed using linear and nonlinear interpolation and projected with ANN-based regression modeling. Mann-Kendall trend analysis was applied to assess the temporal consistency of ANN-predicted CN and Tlag series and support the selection of the most reliable synthetic datasets. The simulations revealed highly non-linear growth in peak discharges, with the Qarah Shur sub-basin projected to experience a 163.78% increase in flood intensity by 2050, resulting from a substantial increase in the contribution of barren land to flood intensification (from 22.89% to 78.88%) and a threefold increase in the contribution of sparse vegetation (from 9.04% to 31.13%). These two factors, individually and in combination, were identified as the most critical contributors to flood amplification in this sub-basin. The Galikesh sub-basin followed with a 68.99% increase by 2050, being the only sub-basin where the contribution of road expansion (from 8.88% to 27.41%) emerged as the dominant factor driving flood intensification. The Tamar Gorgan and Tangerah sub-basins demonstrated more moderate increases in discharge, 13.89% and 10.44%, respectively, driven by modest land degradation and road building. This work’s findings underscore growing risks to infrastructure, including embankment instability, bridge scour, and urban inundation, and to groundwater recharge, aquatic ecosystems, and water quality through the mobilization of pollutants and sediments under increasing flood risk. The spatial comparison of sub-basin-level responses captures the heterogeneity of flood vulnerability and the urgency of instituting adaptive land use policies grounded in hydrological sensitivity, riparian corridor protection, and nature-based flood mitigation.

Abstract: We present the first long-term analysis of Classics instruction in California’s community colleges, drawing on a dataset of nearly 47,000 course sections offered between 1993 and 2024. While the field of Classics has been the subject of ongoing discussion and restructuring in four-year institutions, its presence in two-year colleges, where nearly 40% of US undergraduates are enrolled, has received far less attention. We identify three distinct phases in the curricular history of Classics in the California Community Colleges (CCC) system: growth through the 1990s and early 2000s; a sharp contraction in 2007–2008; and a prolonged period of reduced offerings from 2008 to 2024. The data reveal a 38% overall decline in annual Classics-related course sections since 2008, with steep reductions in Latin, which fell by 86%. Our analysis shows that specialised areas with limited general education alignment were disproportionately affected. We argue that institutional changes triggered by budget cuts, policy reforms, and a shift towards transfer credit efficiency and workforce alignment have narrowed the curricular space for the humanities, particularly in disciplines lacking perceived vocational application. The erosion of Classics offerings raises urgent questions about educational equity, transfer access, and the future of humanities education in open-access institutions. This article is the first in an envisaged series of articles about Classics in California community colleges.

Online hate speech targeting minority groups poses a significant social identity threat to those affected, often prompting coping strategies like out-group avoidance or derogation, which can harm intergroup relations in diverse societies. However, collective action as an empowering response has received limited attention in this context. Drawing on the social identity model of collective action (SIMCA), this study examines both the mechanisms and boundary conditions of online hate speech’s impact on minority groups. Across two experiments ( N Study1 = 328 Muslims; N Study2 = 583 Latinos), we find that online hate speech increases group-based injustice, which in turn drives collective action intentions, supporting the SIMCA. This effect is amplified when information embedded in the online hate speech is perceived as inaccurate or flagged for inaccuracy. In Study 2, we further demonstrate that counter-speech can reduce group-based injustice and, when paired with low-accuracy hate speech, enhance collective efficacy beliefs.